At the mid eighties researchers in computational learning theory pointed the way to examples of hard learning tasks such as learning parity with noise (LPN) and learning with errors (LWE) which have been extremely useful for building sophisticated cryptographic primitives such as homomorphic encryption which are unbreakable if LPN and LWE are hard to learn.

Today, with the rise of machine learning algorithms that use large amounts of data to come up with procedures which have the potential to replace human decision processes, cryptography, in turn, stands to provide machine learning, tools for keeping data private during both training and inference phases of ML and to provide methods to verify adherence of models with data. These promise to be important steps in ensuring the safe transition of power from human to algorithmic decision making.